Overview

Dataset statistics

Number of variables12
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.9 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Alerts

alcohol is highly overall correlated with qualityHigh correlation
quality is highly overall correlated with alcoholHigh correlation
residual sugar has unique valuesUnique
chlorides has unique valuesUnique
free sulfur dioxide has unique valuesUnique

Reproduction

Analysis started2023-04-03 09:39:49.415536
Analysis finished2023-04-03 09:40:16.359073
Duration26.94 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct417
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.15253
Minimum3.32
Maximum11.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:16.438981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.32
5-th percentile5.149
Q16.3775
median7.15
Q38
95-th percentile9.13
Maximum11.49
Range8.17
Interquartile range (IQR)1.6225

Descriptive statistics

Standard deviation1.2015976
Coefficient of variation (CV)0.16799616
Kurtosis-0.019292121
Mean7.15253
Median Absolute Deviation (MAD)0.805
Skewness-0.028878576
Sum7152.53
Variance1.4438367
MonotonicityNot monotonic
2023-04-03T16:40:16.590977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.54 11
 
1.1%
7.7 8
 
0.8%
7.39 8
 
0.8%
7.05 8
 
0.8%
7.19 7
 
0.7%
8.1 7
 
0.7%
6.77 7
 
0.7%
7.56 7
 
0.7%
6.4 7
 
0.7%
6.78 7
 
0.7%
Other values (407) 923
92.3%
ValueCountFrequency (%)
3.32 1
0.1%
3.4 1
0.1%
3.84 1
0.1%
3.85 2
0.2%
4.04 1
0.1%
4.13 1
0.1%
4.27 2
0.2%
4.29 2
0.2%
4.47 1
0.1%
4.5 1
0.1%
ValueCountFrequency (%)
11.49 1
0.1%
10.75 1
0.1%
10.52 1
0.1%
10.32 1
0.1%
10.27 1
0.1%
10.07 1
0.1%
10 1
0.1%
9.9 1
0.1%
9.89 1
0.1%
9.85 1
0.1%

volatile acidity
Real number (ℝ)

Distinct879
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5208385
Minimum0.1399
Maximum0.8051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:16.720204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1399
5-th percentile0.357665
Q10.4561
median0.52485
Q30.585375
95-th percentile0.67271
Maximum0.8051
Range0.6652
Interquartile range (IQR)0.129275

Descriptive statistics

Standard deviation0.095848274
Coefficient of variation (CV)0.18402686
Kurtosis0.1618529
Mean0.5208385
Median Absolute Deviation (MAD)0.065
Skewness-0.1976987
Sum520.8385
Variance0.0091868916
MonotonicityNot monotonic
2023-04-03T16:40:16.832215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5546 4
 
0.4%
0.4796 3
 
0.3%
0.6198 3
 
0.3%
0.5386 3
 
0.3%
0.6486 3
 
0.3%
0.5397 3
 
0.3%
0.6158 3
 
0.3%
0.5754 3
 
0.3%
0.5214 3
 
0.3%
0.5132 3
 
0.3%
Other values (869) 969
96.9%
ValueCountFrequency (%)
0.1399 1
0.1%
0.1613 1
0.1%
0.227 1
0.1%
0.2359 1
0.1%
0.2366 1
0.1%
0.2419 1
0.1%
0.2562 1
0.1%
0.2697 1
0.1%
0.2837 1
0.1%
0.2848 1
0.1%
ValueCountFrequency (%)
0.8051 1
0.1%
0.7756 1
0.1%
0.7639 1
0.1%
0.7571 1
0.1%
0.7473 1
0.1%
0.7471 1
0.1%
0.7417 1
0.1%
0.7414 1
0.1%
0.7366 1
0.1%
0.7358 1
0.1%

citric acid
Real number (ℝ)

Distinct769
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.270517
Minimum0.1167
Maximum0.4096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:16.961907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1167
5-th percentile0.18957
Q10.2378
median0.2722
Q30.302325
95-th percentile0.353115
Maximum0.4096
Range0.2929
Interquartile range (IQR)0.064525

Descriptive statistics

Standard deviation0.049098371
Coefficient of variation (CV)0.18149828
Kurtosis-0.10467925
Mean0.270517
Median Absolute Deviation (MAD)0.0324
Skewness-0.045576059
Sum270.517
Variance0.0024106501
MonotonicityNot monotonic
2023-04-03T16:40:17.171208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3019 5
 
0.5%
0.2378 4
 
0.4%
0.2743 4
 
0.4%
0.2691 4
 
0.4%
0.296 4
 
0.4%
0.2703 4
 
0.4%
0.244 4
 
0.4%
0.2835 4
 
0.4%
0.2747 4
 
0.4%
0.2758 3
 
0.3%
Other values (759) 960
96.0%
ValueCountFrequency (%)
0.1167 1
0.1%
0.124 1
0.1%
0.1264 1
0.1%
0.1363 1
0.1%
0.1423 1
0.1%
0.1514 1
0.1%
0.1522 1
0.1%
0.1524 1
0.1%
0.1549 2
0.2%
0.1565 1
0.1%
ValueCountFrequency (%)
0.4096 1
0.1%
0.4002 1
0.1%
0.3994 1
0.1%
0.3974 1
0.1%
0.3961 1
0.1%
0.391 1
0.1%
0.3897 1
0.1%
0.3891 1
0.1%
0.3878 1
0.1%
0.3875 1
0.1%

residual sugar
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5671037
Minimum0.032554525
Maximum5.5507549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:17.292414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.032554525
5-th percentile0.93793933
Q11.8963299
median2.5194303
Q33.2208735
95-th percentile4.2570011
Maximum5.5507549
Range5.5182004
Interquartile range (IQR)1.3245435

Descriptive statistics

Standard deviation0.98791544
Coefficient of variation (CV)0.38483659
Kurtosis-0.042980034
Mean2.5671037
Median Absolute Deviation (MAD)0.66116903
Skewness0.13263809
Sum2567.1037
Variance0.97597691
MonotonicityNot monotonic
2023-04-03T16:40:17.404733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.049401312 1
 
0.1%
4.166318801 1
 
0.1%
3.642119103 1
 
0.1%
3.004778892 1
 
0.1%
1.490662927 1
 
0.1%
2.36282942 1
 
0.1%
1.514715338 1
 
0.1%
1.991966471 1
 
0.1%
3.07522532 1
 
0.1%
2.665885088 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.03255452502 1
0.1%
0.0333329319 1
0.1%
0.05177378193 1
0.1%
0.07715577137 1
0.1%
0.0847444565 1
0.1%
0.1434249636 1
0.1%
0.2252879074 1
0.1%
0.233060856 1
0.1%
0.2738022623 1
0.1%
0.3216899123 1
0.1%
ValueCountFrequency (%)
5.550754935 1
0.1%
5.299523947 1
0.1%
5.252864283 1
0.1%
5.217428733 1
0.1%
5.210259585 1
0.1%
5.154947295 1
0.1%
5.056969702 1
0.1%
5.052230163 1
0.1%
5.040514262 1
0.1%
4.986085021 1
0.1%

chlorides
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081195153
Minimum0.015122439
Maximum0.14075757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:17.533377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.015122439
5-th percentile0.048996373
Q10.066573632
median0.082166902
Q30.095311501
95-th percentile0.1137751
Maximum0.14075757
Range0.12563513
Interquartile range (IQR)0.02873787

Descriptive statistics

Standard deviation0.020110647
Coefficient of variation (CV)0.24768286
Kurtosis-0.24650814
Mean0.081195153
Median Absolute Deviation (MAD)0.013969815
Skewness-0.051319297
Sum81.195153
Variance0.00040443813
MonotonicityNot monotonic
2023-04-03T16:40:17.654202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.07057448961 1
 
0.1%
0.08069023448 1
 
0.1%
0.1048101934 1
 
0.1%
0.09814548754 1
 
0.1%
0.04689424649 1
 
0.1%
0.09548918967 1
 
0.1%
0.05912332691 1
 
0.1%
0.06128837225 1
 
0.1%
0.09591411199 1
 
0.1%
0.06923676388 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.01512243917 1
0.1%
0.02079355779 1
0.1%
0.02425894688 1
0.1%
0.02720863888 1
0.1%
0.03211075476 1
0.1%
0.03356638826 1
0.1%
0.03428309083 1
0.1%
0.03436967289 1
0.1%
0.03446245762 1
0.1%
0.03475166116 1
0.1%
ValueCountFrequency (%)
0.1407575694 1
0.1%
0.1357897348 1
0.1%
0.1353679611 1
0.1%
0.1336557588 1
0.1%
0.1314254504 1
0.1%
0.1278936185 1
0.1%
0.1278015011 1
0.1%
0.1274375809 1
0.1%
0.1272041614 1
0.1%
0.1268507396 1
0.1%

free sulfur dioxide
Real number (ℝ)

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.907679
Minimum0.19467852
Maximum27.462525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:17.766213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.19467852
5-th percentile7.0257029
Q111.426717
median14.860346
Q318.313098
95-th percentile23.338396
Maximum27.462525
Range27.267847
Interquartile range (IQR)6.886381

Descriptive statistics

Standard deviation4.8880997
Coefficient of variation (CV)0.32789139
Kurtosis-0.36496364
Mean14.907679
Median Absolute Deviation (MAD)3.4455236
Skewness0.007130416
Sum14907.679
Variance23.893519
MonotonicityNot monotonic
2023-04-03T16:40:17.871924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.59381751 1
 
0.1%
9.190047046 1
 
0.1%
16.27723942 1
 
0.1%
14.30579625 1
 
0.1%
8.484672533 1
 
0.1%
15.21949967 1
 
0.1%
18.56961122 1
 
0.1%
10.95890108 1
 
0.1%
26.56253402 1
 
0.1%
8.659285965 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0.1946785233 1
0.1%
0.6216278843 1
0.1%
0.8601765769 1
0.1%
3.032138635 1
0.1%
3.129885479 1
0.1%
3.535256825 1
0.1%
3.769986335 1
0.1%
3.971302983 1
0.1%
3.980429158 1
0.1%
4.03475929 1
0.1%
ValueCountFrequency (%)
27.46252542 1
0.1%
27.00630706 1
0.1%
26.8226265 1
0.1%
26.66577325 1
0.1%
26.63048976 1
0.1%
26.56253402 1
0.1%
26.25821137 1
0.1%
26.0154425 1
0.1%
25.7112297 1
0.1%
25.68593447 1
0.1%

total sulfur dioxide
Real number (ℝ)

Distinct881
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.29015
Minimum3.15
Maximum69.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:17.986607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.15
5-th percentile24.3625
Q133.785
median40.19
Q347.0225
95-th percentile56.3125
Maximum69.96
Range66.81
Interquartile range (IQR)13.2375

Descriptive statistics

Standard deviation9.9657674
Coefficient of variation (CV)0.24734997
Kurtosis0.063949789
Mean40.29015
Median Absolute Deviation (MAD)6.705
Skewness-0.024060027
Sum40290.15
Variance99.316519
MonotonicityNot monotonic
2023-04-03T16:40:18.106699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.61 3
 
0.3%
41.05 3
 
0.3%
44.51 3
 
0.3%
35.2 3
 
0.3%
39.64 3
 
0.3%
37.25 3
 
0.3%
41.59 3
 
0.3%
45.81 2
 
0.2%
45.32 2
 
0.2%
40.32 2
 
0.2%
Other values (871) 973
97.3%
ValueCountFrequency (%)
3.15 1
0.1%
4.3 1
0.1%
11.55 1
0.1%
13.38 1
0.1%
15.56 1
0.1%
16.01 1
0.1%
16.15 1
0.1%
16.56 1
0.1%
16.69 1
0.1%
17.12 1
0.1%
ValueCountFrequency (%)
69.96 1
0.1%
69.22 1
0.1%
67.71 2
0.2%
66.99 1
0.1%
65.41 1
0.1%
65.35 1
0.1%
64.8 1
0.1%
64.66 1
0.1%
64.03 1
0.1%
63.39 1
0.1%

density
Real number (ℝ)

Distinct107
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9959253
Minimum0.9888
Maximum1.0026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:18.236341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.9888
5-th percentile0.992695
Q10.9946
median0.996
Q30.9972
95-th percentile0.999205
Maximum1.0026
Range0.0138
Interquartile range (IQR)0.0026

Descriptive statistics

Standard deviation0.0020201809
Coefficient of variation (CV)0.0020284463
Kurtosis0.016365621
Mean0.9959253
Median Absolute Deviation (MAD)0.0013
Skewness-0.076882789
Sum995.9253
Variance4.081131 × 10-6
MonotonicityNot monotonic
2023-04-03T16:40:18.341431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9961 25
 
2.5%
0.9959 25
 
2.5%
0.9965 25
 
2.5%
0.997 25
 
2.5%
0.996 24
 
2.4%
0.9966 24
 
2.4%
0.9953 23
 
2.3%
0.9948 23
 
2.3%
0.9967 22
 
2.2%
0.9957 22
 
2.2%
Other values (97) 762
76.2%
ValueCountFrequency (%)
0.9888 1
 
0.1%
0.9896 1
 
0.1%
0.9905 3
0.3%
0.9906 1
 
0.1%
0.9908 2
0.2%
0.9909 1
 
0.1%
0.991 1
 
0.1%
0.9911 2
0.2%
0.9912 2
0.2%
0.9915 2
0.2%
ValueCountFrequency (%)
1.0026 1
0.1%
1.0017 1
0.1%
1.0014 1
0.1%
1.0012 1
0.1%
1.0011 1
0.1%
1.0008 2
0.2%
1.0007 1
0.1%
1.0006 2
0.2%
1.0005 1
0.1%
1.0004 2
0.2%

pH
Real number (ℝ)

Distinct61
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.30361
Minimum2.97
Maximum3.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:18.464365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.97
5-th percentile3.14
Q13.23
median3.3
Q33.37
95-th percentile3.4705
Maximum3.71
Range0.74
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.10487548
Coefficient of variation (CV)0.031745721
Kurtosis0.080909552
Mean3.30361
Median Absolute Deviation (MAD)0.07
Skewness0.1476726
Sum3303.61
Variance0.010998867
MonotonicityNot monotonic
2023-04-03T16:40:18.576454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.34 43
 
4.3%
3.32 42
 
4.2%
3.23 39
 
3.9%
3.33 39
 
3.9%
3.25 39
 
3.9%
3.35 39
 
3.9%
3.21 37
 
3.7%
3.29 37
 
3.7%
3.3 36
 
3.6%
3.26 35
 
3.5%
Other values (51) 614
61.4%
ValueCountFrequency (%)
2.97 1
 
0.1%
3.01 1
 
0.1%
3.03 1
 
0.1%
3.04 3
 
0.3%
3.05 1
 
0.1%
3.06 3
 
0.3%
3.07 1
 
0.1%
3.08 3
 
0.3%
3.09 4
0.4%
3.1 8
0.8%
ValueCountFrequency (%)
3.71 1
 
0.1%
3.66 1
 
0.1%
3.6 1
 
0.1%
3.58 4
0.4%
3.57 3
0.3%
3.56 2
 
0.2%
3.55 2
 
0.2%
3.54 2
 
0.2%
3.53 6
0.6%
3.52 3
0.3%

sulphates
Real number (ℝ)

Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59839
Minimum0.29
Maximum0.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:18.803220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile0.44
Q10.53
median0.595
Q30.67
95-th percentile0.77
Maximum0.96
Range0.67
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.10081901
Coefficient of variation (CV)0.16848378
Kurtosis0.064819282
Mean0.59839
Median Absolute Deviation (MAD)0.065
Skewness0.1491989
Sum598.39
Variance0.010164472
MonotonicityNot monotonic
2023-04-03T16:40:18.909354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59 53
 
5.3%
0.55 46
 
4.6%
0.61 42
 
4.2%
0.56 42
 
4.2%
0.66 39
 
3.9%
0.54 39
 
3.9%
0.64 36
 
3.6%
0.67 35
 
3.5%
0.63 35
 
3.5%
0.62 33
 
3.3%
Other values (50) 600
60.0%
ValueCountFrequency (%)
0.29 1
 
0.1%
0.33 3
0.3%
0.35 2
 
0.2%
0.36 1
 
0.1%
0.37 3
0.3%
0.38 5
0.5%
0.39 2
 
0.2%
0.4 5
0.5%
0.41 7
0.7%
0.42 5
0.5%
ValueCountFrequency (%)
0.96 1
 
0.1%
0.94 1
 
0.1%
0.91 1
 
0.1%
0.9 1
 
0.1%
0.88 1
 
0.1%
0.87 2
0.2%
0.86 2
0.2%
0.85 1
 
0.1%
0.84 2
0.2%
0.83 3
0.3%

alcohol
Real number (ℝ)

Distinct490
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.59228
Minimum6.03
Maximum15.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:19.038317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.03
5-th percentile8.04
Q19.56
median10.61
Q311.6225
95-th percentile13
Maximum15.02
Range8.99
Interquartile range (IQR)2.0625

Descriptive statistics

Standard deviation1.510706
Coefficient of variation (CV)0.14262331
Kurtosis-0.13173156
Mean10.59228
Median Absolute Deviation (MAD)1.035
Skewness-0.018991404
Sum10592.28
Variance2.2822326
MonotonicityNot monotonic
2023-04-03T16:40:19.154649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.31 7
 
0.7%
9.86 7
 
0.7%
10.58 6
 
0.6%
11.67 6
 
0.6%
10.02 6
 
0.6%
9.94 6
 
0.6%
10.15 6
 
0.6%
11.32 6
 
0.6%
11.09 6
 
0.6%
11 6
 
0.6%
Other values (480) 938
93.8%
ValueCountFrequency (%)
6.03 1
0.1%
6.08 1
0.1%
6.22 1
0.1%
6.38 1
0.1%
6.42 1
0.1%
6.48 1
0.1%
6.58 1
0.1%
6.65 1
0.1%
6.76 1
0.1%
6.95 1
0.1%
ValueCountFrequency (%)
15.02 1
0.1%
14.56 1
0.1%
14.45 1
0.1%
14.43 1
0.1%
14.35 1
0.1%
14.34 1
0.1%
14.23 1
0.1%
14.22 1
0.1%
14.12 1
0.1%
14.07 1
0.1%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.958
Minimum5
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-04-03T16:40:19.244736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q17
median8
Q39
95-th percentile9
Maximum10
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.90280178
Coefficient of variation (CV)0.11344581
Kurtosis0.108291
Mean7.958
Median Absolute Deviation (MAD)1
Skewness-0.089054091
Sum7958
Variance0.81505105
MonotonicityNot monotonic
2023-04-03T16:40:19.332761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 449
44.9%
7 248
24.8%
9 223
22.3%
6 38
 
3.8%
10 37
 
3.7%
5 5
 
0.5%
ValueCountFrequency (%)
5 5
 
0.5%
6 38
 
3.8%
7 248
24.8%
8 449
44.9%
9 223
22.3%
10 37
 
3.7%
ValueCountFrequency (%)
10 37
 
3.7%
9 223
22.3%
8 449
44.9%
7 248
24.8%
6 38
 
3.8%
5 5
 
0.5%

Interactions

2023-04-03T16:40:14.754581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:50.048204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:54.264784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:58.310554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:02.423868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.727597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.176840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.833503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.503735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.765971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.097286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.418453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.859102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:50.358448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:54.631250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:58.639667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:02.791760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.831597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.316590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.970120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.618198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.862157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.214303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.517761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.964195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:50.647947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:54.915753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:58.985867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:03.112038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.936332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.455753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.108967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.722991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.056107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.318506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.615980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.061327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:50.948120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:55.302018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:59.298298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:03.407906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.032266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.583354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.255415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.827860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.169487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.434203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.713728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.168575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:51.381630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:55.616559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:59.657798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:03.697854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.144286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.711930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.391878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.924832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.275539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.541179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.843530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.267061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:51.848210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:55.960459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:59.998430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:03.991225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.256322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.832411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.532340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.037419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.389080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.655489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.949292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.364564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:52.168188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:56.275788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:00.314722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.103692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.384467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.954796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.676585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.141410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.487725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.770229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.048474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.471284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:52.529248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:56.635518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:00.648066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.215633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.520792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.099678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.815175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.253342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.603405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.874848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.148102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.585702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:52.876359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:56.981612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:00.975713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.319606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.658377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.338626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:08.983469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.358745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.700019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:12.987067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.253116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.699133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:53.248286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:57.334840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:01.314736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.423698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.786846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.464784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.130842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.473007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.804418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.108327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.465121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.798109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:53.614670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:57.655650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:01.631090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.535604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:05.906834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.604518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.267011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.581649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.900887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.213902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.569123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:15.886360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:53.931819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:39:57.976347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:02.081283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:04.631597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:06.046020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:07.713290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:09.389791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:10.669958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:11.999406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:13.304507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-03T16:40:14.658630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-03T16:40:19.421788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
fixed acidity1.000-0.029-0.047-0.0210.0150.0050.0390.002-0.013-0.0210.0090.392
volatile acidity-0.0291.000-0.024-0.017-0.0110.0160.0090.022-0.0260.062-0.005-0.013
citric acid-0.047-0.0241.0000.011-0.018-0.0570.0410.018-0.0250.014-0.034-0.048
residual sugar-0.021-0.0170.0111.0000.040-0.011-0.041-0.024-0.008-0.012-0.0290.185
chlorides0.015-0.011-0.0180.0401.0000.0270.0080.0140.0200.0140.0520.044
free sulfur dioxide0.0050.016-0.057-0.0110.0271.000-0.0310.020-0.0160.0000.0720.038
total sulfur dioxide0.0390.0090.041-0.0410.008-0.0311.0000.0420.0450.052-0.065-0.048
density0.0020.0220.018-0.0240.0140.0200.0421.000-0.0200.0510.0020.016
pH-0.013-0.026-0.025-0.0080.020-0.0160.045-0.0201.000-0.0450.021-0.004
sulphates-0.0210.0620.014-0.0120.0140.0000.0520.051-0.0451.000-0.050-0.045
alcohol0.009-0.005-0.034-0.0290.0520.072-0.0650.0020.021-0.0501.0000.807
quality0.392-0.013-0.0480.1850.0440.038-0.0480.016-0.004-0.0450.8071.000

Missing values

2023-04-03T16:40:16.025985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-03T16:40:16.232043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
05.900.44510.18132.0494010.07057416.59381842.270.99823.270.718.647
18.400.57680.20993.1095900.10168122.55551916.010.99603.350.5710.038
27.540.59180.32483.6737440.0724169.31686635.520.99903.310.649.238
35.390.42010.31313.3718150.07275518.21230041.970.99453.340.5514.079
46.510.56750.19404.4047230.0663799.36059146.270.99253.270.4511.498
59.180.33320.24762.6334910.08230412.23217051.050.99653.400.6310.828
64.290.49970.29323.7818440.07964910.15238944.260.99693.470.449.767
76.690.40660.28961.3408130.08325311.33020029.390.99993.140.688.887
88.720.43920.18863.3705020.08205718.14581038.490.99473.270.5910.518
96.160.39240.28302.2903650.04885015.51735958.290.99693.370.519.967
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
9906.660.41200.32622.3968580.05918718.63362928.230.99863.140.4910.518
9918.400.53610.22460.7386870.09907714.46570860.781.00033.240.4712.529
9925.620.45970.28682.3148330.04179621.44584941.540.99803.270.7111.238
9934.880.58990.27213.0040770.09556922.28886445.740.99473.230.798.576
9946.700.70470.21833.8545670.05884119.88609239.040.99953.270.6613.439
9957.960.60460.26621.5920480.05755514.89244544.610.99753.350.5410.418
9968.480.40800.22270.6819550.05162723.54896525.830.99723.410.469.918
9976.110.48410.37202.3772670.04280621.62458548.750.99283.230.559.947
9987.760.35900.32084.2944860.09827612.74618644.530.99523.300.669.768
9995.870.52140.18832.1794900.05292316.20386424.370.99833.290.7010.177